Blood flow mapping provides important information for the diagnosis and treatment of many diseases, such as stroke and atherosclerosis. Doppler ultrasound (US) is a frequently used technique to measure blood flow in humans. However, because of the poor ultrasonic scattering contrast between blood and extravascular tissue, Doppler US cannot measure slow blood flow, which limits its use to evaluating blood flow in the major arteries and veins. Optical visualization methods, such as Doppler optical coherence tomography and laser speckle flowmetry, cannot measure blood flow in humans in the optical diffusive regime due to the limited penetration of ballistic photons in biological tissues.
With high blood detection contrast and deep penetrative reach, photoacoustic tomography (PAT) may provide a way to measure slow blood flow in the diffusive regime in humans. In PAT, short light pulses, usually from a laser, excite the target. Following absorption of the light, an initial temperature rise induces a pressure rise due to the photoacoustic (PA) effect. The pressure rise then propagates as a PA wave and is finally detected by an ultrasonic transducer. Each laser pulse yielded a one-dimensional depth-resolved PA image (A-line) by recording the time course of PA signals. Because blood absorbs visible light much more strongly than most other tissue components, PAT can detect blood with high contrast. In addition, by detecting ultrasonic signals, which have much lower scattering than optical signals in tissue, PAT can image deep tissues and structures with high spatial resolution. For example, PAT has detected blood vessels in vivo at depths as high as 3.5 cm.
Many PAT-based methods have been proposed to measure blood flow. Doppler PA detects absorption-based signals and calculates the flow velocity based on the frequency shift of these signals. However, Doppler PA is most effective for flows containing sparse particles, and the accuracy of Doppler PA is reduced when the detection axis is perpendicular to the flow direction. M-mode PA flowmetry quantifies the “slow-time” PA amplitudes, defined as a series of maximum PA amplitudes obtained from an A-line sequence, and estimates the flow speed by changes in the “slow-time” PA amplitudes induced by particle movement. M-mode PA flowmetry enabled measuring flow speeds perpendicular to the detection axis. Based on similar ideas, time-domain PA auto-correlation and frequency-domain PA Doppler bandwidth broadening have been used to measure blood flow in mice in vivo. To eliminate the measurement error resulting from the particle size, cross-correlation based PA flowmetry was also demonstrated in mice. For human imaging, however, because vessels are often more deeply embedded than they are in mice, PAT imaging of human vessels is characterized by significantly degraded spatial resolutions. As the detection voxel size increases, there is a corresponding decrease in the slow-time PA signal changes due to the flowing particles or red blood cells. When these changes in the slow-time PA signal changes due to the flowing particles are smaller than other PA signal changes induced by, for example, thermal noise, the extraction of flow information from the changes in the slow-time PA signals may be challenging.
Photoacoustic tomography (PAT) is a modality that provides imaging in either two dimensions (2D) or three dimensions (3D). Combining the advantages of optical excitation and acoustic detection, PAT can image rich optical absorption contrast in biological tissues at depths. To date, PAT has been widely used for both structural and functional biological imaging in many different fields, including hematology, oncology, dermatology, ophthalmology, and gastroenterology. Depending on the limiting factor for spatial resolution, PAT can be divided into optical-resolution PAT (OR-PAT) and acoustic-resolution PAT (AR-PAT). In OR-PAT, the optical focus is much tighter than the acoustic focus, and a high spatial resolution can be achieved. AR-PAT provides a lower spatial resolution, defined by the dimensions of acoustic focus achieved by the acoustic transducers. Nevertheless, because in biological tissue ultrasound suffers much less scattering than light, AR-PAT can achieve deep imaging with a depth-to-resolution ratio of more than 100. So far, with high resolution, OR-PAT has imaged a variety of important biological parameters in vivo, such as the oxygen saturation of hemoglobin (sO2), pulse wave velocity, and the metabolic rate of oxygen (MRO2). However, although AR-PAT has imaged sO2 at depths corresponding to deep vessels, it still cannot provide in vivo blood flow information. In addition, in order to calculate MRO2 in deep vessels with PAT, blood flow speed needs to be measured, which makes it even more important to quantify flow.
There are two fundamental reasons why it is difficult for AR-PAT to measure blood flow velocity. First, unlike ultrasound, PAT almost has no speckles. If the target has a smooth boundary with respect to the wavelengths of the PA waves, the boundary signals of the target will stand out, while the speckles inside the target are largely suppressed. Because blood vessels in biological tissues typically have smooth boundaries, it is challenging for PAT to extract blood flow information based on speckle fluctuations. Second, AR-PAT has a lower spatial resolution than OR-PAT and thus a larger detection voxel size. In typical OR-PAT imaging, the spatial resolution is comparable to the size of red blood cells (RBCs). Thus, when RBCs flow into and out of a detection voxel, the PA signal changes are observable. By monitoring how fast the signal changes, the flow velocity can be calculated. However, in AR-PAT, the large detection voxel contains many more RBCs than in OR-PAT. Because the number of RBCs inside the detection voxel can be assumed to follow a Poisson distribution, a larger mean number of RBCs leads to a smaller relative RBC number change and thus a smaller PA signal change. For example, if there are 10,000 RBCs in the detection voxel, the PA signal change due to the RBC number change would be only around 1%, so the AR-PAT system would need a signal-to-noise ratio (SNR) of more than 100 to measure the flow velocity.
Although challenging, different methods have been proposed to achieve blood flow measurement with AR-PAT, including PA Doppler (PAD) flowmetry and ultrasonically encoded PA flowgraphy (UE-PAF). Based on the PAD effect, different PAD shifts have been observed from particles moving with different flow speeds, and the Doppler theory allows the flow speeds to be calculated. However, to observe the PAD shift, the moving particles have to be very sparse. Thus, this method cannot measure the flow velocity of whole blood. But by using ultrasound to encode the PA signals, UE-PAF can achieve whole blood flow imaging in deep tissue. In UE-PAF, modulated ultrasound is focused into the blood vessel to create a heating source. Because PA signals are proportional to temperature, the PA signals from the heated area will increase. By monitoring the increased PA signals along the blood vessel, the flow speed in the blood vessel can be measured. However, this method's complexity has limited it to only phantoms, and so far, no in vivo data have been reported.
A need exists for a method of measuring slow blood flow within relatively deep vessels of human subjects using a PAT-based method.